The purpose of this qualitative study is to explore the experience and self-management of fatigue in patients on hemodialysis. Patients with end-stage renal disease (ESRD), a common, chronic illness that requires dialysis or kidney transplant and affects over 525,000 people in the United States, identify fatigue as one of the most prevalent symptoms with which they contend, with a prevalence ranging from 60% to 97%. Patients on hemodialysis account for approximately 92% of the overall dialysis population and they may experience fatigue as a symptom of ESRD, or as a result of the hemodialysis treatment itself. Fatigue in patients on hemodialysis has been associated with lower quality of life and lower survival rates. Management for symptoms such as fatigue is an important aspect of self-care for hemodialysis patients, as engaging in effective management techniques leads to longer lives for these patients. However, there is little information about the fatigue experience for hemodialysis patients or how they manage their fatigue and how these strategies evolve over time. Studying the experience and self-management of fatigue in patients on hemodialysis is critical to the development of techniques that will help alleviate fatigue for these patients. Methods: Qualitative descriptive methods, specifically naturalistic inquiry, will be used to explore the experience and self-management of fatigue in patients new to hemodialysis. Participants will be asked to complete a fatigue diary beginning on the morning of a dialysis day and ending on the morning of the next dialysis day (48 hours). Following completion of the fatigue diary, in-depth interviews will be conducted exploring the experience and self-management of fatigue. Training Plan: The training plan is designed to further my knowledge in the areas of renal disease, dialysis, fatigue, and qualitative methods and prepare me to participate in research design and implementation by taking part in coursework, research conferences, ethical training, research practica, and data analysis.